Feature Selection: An Exploration of Algorithm Performance

Introduction

What is feature selection? Why do we care?

• Feature selection is a crucial step in data preprocessing, especially in machine learning and statistical modeling. It involves selecting a subset of relevant features (variables, predictors) for building a model.
• It is important because it reduces the risk of overfitting and enhances the performance of machine learning models and pattern recognition systems.

Introduction: Early Techniques

Forward, backward, and stepwise variable selection in linear models

• The forward method starts with no variables in the model, and we will add them one by one until no improvement is shown.
• Backward is when we start with all variables and remove the least significance iteratively.
• Stepwise selection combines both approaches, iteratively adding and removing variables to optimize the models performance.

Introduction: Early Techniques

Univariate screening procedures (USP)

• USP represents an important milestone for feature selection.
• These methods involve evaluating each predictor variable individually to determine its relationship with the target variable. The process selects variables that meet a specific statistical threshold.
• While these methods are simple and quick to use, they often miss the complex connections between variables.

Introduction: Modern Techniques

Advanced feature selection methods

• Similarity-based approaches select features based on their similarity or dissimilarity.
• Information-theoretical-based approaches use concepts like entropy and mutual information to evaluate feature importance.
• Sparse-learning-based approaches focus on identifying a small, essential set of features by limiting the number of features selected.
• Statistical-based approaches utilize statistical tests and models to determine the significance of features for predicting the target variable.

Introduction: Modern Techniques

Classification of methods

• The filter model selects features based on the general properties of the training data, independent of any learning algorithm, making it computationally efficient for large datasets.
• The wrapper model, on the other hand, uses a specific learning algorithm to evaluate and determine which features to keep, often leading to better performance but at a higher computational cost.
• Embedded methods perform feature selection during the model training process, integrating selection directly with learning.
• Hybrid methods combine the best aspects of filter and wrapper methods to achieve optimal performance with manageable computational complexity.

Methods: Correlation - Based Feature Selection (CFS)

• Filter method
• Uses the correlation coefficient to measure the relationship between each variable and the target, independently.
• Features that are highly correlated are considered redundant.

CFS’s feature subset evaluation function is:

\(\text{Ms} = \frac{k\bar{\text{rcf}}}{\sqrt k + k(k - 1)\bar{\text{rff}}}\)

Methods: Recursive Feature Elimination (RFE)

• Wrapper method.
• Removes variables iteratively.

Steps:

1. Train the classifier.
2. compute the ranking criterion for all features.
3. remove the feature(s) with smallest ranking values.
4. Repeat until desired number of features selected.

• optimal subset of features - one that provides the highest accuracy

Methods: Least Absolute Shrinkage and Selection Operator (LASSO)

• Embedded method
• LASSO finds the coefficient of features
• Uses L1 Regularization to shrink coefficients to zero,

The LASSO estimate is defined by the solution to the l1 optimization problem

minimize \(\frac{\| Y - X\beta \|_2^2}{n}\) subject to \(X \sum_{j=1}^{k} \|\beta_j\|_1 < t\)

where \(t\) is the upper bound for the sum of the coefficients

Methods: CFS & RFE

• Hybrid method.
• Combines filter and wrapper methods.

Step 1: Filtering using the correlation coefficient. (e.g. trim the “low hanging fruit”)

Step 2: Remove remaining variables iteratively using RFE.

Analysis: Spambase Dataset

• 4601 instances of emails
• 57 features for classification tasks
• Binary classification: email is spam (1) or not (0)
• 80/20 train/test split
• Increased number of features by adding all \((\binom{57}{2} = 1,596)\) two-way interactions

Analysis: Spambase Dataset

Figure 1. Frequency of spam targets.

Analysis: COVID-19 NLP Text Classification Dataset

• 45k tweets related to COVID-19, labeled for sentiment analysis.
• Five Sentiment label classes, ranging from extremely positive to extremely negative
• Recoded to a binary classification task, positive (1) or negative (0)
• 33,444 (91%) training and 3,179 (9%) testing records after recoding

Analysis: COVID-19 NLP Text Classification Dataset

Figure 2. Visualization of common words.

Analysis: COVID-19 NLP Text Classification Dataset

Figure 3. Frequency of sentiment targets.

Analysis: COVID-19 NLP Text Classification Dataset

“Bag of words”

• Text data converted into a matrix of word frequencies
• Each row represents a document
• Each column represents a unique word from the entire corpus
• Large (several thousand variables), sparse (> 99% of values = 0) feature set

Analysis: COVID-19 NLP Text Classification Dataset

Figure 4. Distribution of words per Tweet.

Statistical Modeling

Three metrics:

• Accuracy on the test set
• difference between accuracy on the training and test set (overtraining)
• number of variables selected (model complexity)

5 models:

• Baseline Full logistics model with no feature selection
• CFS Select best of 20 correlation thresholds using cross validation
• RFE Select best of 20 sized subsets using cross validation
• LASSO Select best penalty term using cross validation
• CFS + RFE Remove 20% of variables with lowest correlation, Select best of 20 sized subsets using cross validation

Statistical Modeling

Figure 5. Example of cross-validation accuracy with CFS model for the Sentiment task.

Statistical Modeling

Figure 6. Example of coefficient behavior with LASSO model for the spam task.

Results: Spambase Dataset

Results: COVID-19 NLP Text Classification Dataset

Results: Records per Feature

Summary

Conclusion